Ranking of sensitive positions using empirical mode decomposition and Hilbert Transform

Condition Monitoring is the process of recognizing machine health status, by analyzing the various parameters of machine. For Acoustic Emission based condition monitoring, generally acoustic data needs to be taken from several positions and analyzed, which can be cumbersome and many times economically not viable. Thus there arises a need to define sensitive positions. Sensitive positions are positions which demonstrate relatively better features for fault recognition. Previously, the sensitive positions were found and ranked by analyzing certain statistical parameters of acoustic data. In this paper, the same has been done after extracting envelope of the relevant signal, using Empirical mode decomposition followed by Hilbert Transform. A case study was done on a reciprocating type air compressor for comparing the old and proposed technique for finding sensitive positions. Though similar results were found by both methods in normal conditions, when noise was introduced in some positions, the proposed method was found to be more robust w.r.t. noise.